Reading Between the Lines: A Quantitative Analysis on the Importance of Moneyline Odds in NBA Game Prediction Accuracy

Abstract

Recent developments and popularity in prediction markets, in addition to the increase in recent advertisements of sports gambling, have been very prevalent throughout 2024 and the early months of 2025. In an effort to explore whether or not these spaces could offer consistent profit comparable to other investment techniques, excluding arbitrage opportunities, this work attempts to measure the profitability of NBA sports book wagers by developing a data-driven machine learning model that measures the true probability of an NBA team winning a game on a specific night. This work is novel, in that it places greater emphasis on exploring the relationship that moneyline odds have with game outcomes. First, we perform feature engineering to expand our initial dataset, which contains historical moneyline odds alongside game outcomes, into a multitude of components that are associated with the home team's win rate. We then train our model with data of the first 41 games that each of the 30 NBA teams played in a given season, and utilize machine learning algorithms to predict the true probability a team has of winning a game. This is then compared to the implied probabilities sports book set, which is derived from their listed moneyline odds, to provide novel insights. While the algorithms achieve an average accuracy of 70%, the insight gained from attempting to measure profitability in the first half of the 2022-23 NBA season ultimately lays computational and methodological foundations for analyzing associations with moneyline odds.